How to navigate today’s conversational AI and text generation landscape

OpenAI’s revolutionary chatbot ChatGPT has been all over the news in recent months, prompting tech giants like Google and Baidu to accelerate their AI roadmaps.

ChatGPT is built on OpenAI’s GPT language model and provides a variety of features such as engaging in conversations, answering questions, generating written text, debugging code, analyzing sentiment, translating languages, and much more.

Looking at the technologies of the moment, it seems that nothing is more central to the future of humanity than generative artificial intelligence. The idea of ​​scaling the creation of intelligence through machines will affect everything that happens around us, and the momentum in the generative AI space created by the sudden rise of ChatGPT is inspiring.

How should enterprise business leaders respond to this? We believed that by looking under the hood of ChatGPT and deconstructing the application into its individual capabilities, we could deconstruct the product and enable any sufficiently innovative enterprise to identify the elements most relevant to their strategic purpose. This is how this analysis and research was born.

We analyzed the various functions that ChatGPT provides and created an industry landscape map of companies that perform one or more of these functions. You can think of this as breaking down ChatGPT into its various anatomical parts and finding potential alternatives for each function with its own unique and targeted capabilities. The resulting Text Generative and Conversational AI Landscape is presented below and consists of ten functional categories with a sampling of representative companies for each category.

A text generative and conversational AI landscape for companies with features like ChatGPT

Disrupting the text generative and conversational AI landscape

Generative AI is a term gaining popularity with ChatGPT. It refers to AI technology that can generate original content such as text, image, video, audio and code. Our landscape is focused on the area of ​​text-generating AI, as that is the dominant feature of ChatGPT.

As you can see, language models are at the bottom of the landscape because they form the basic building blocks of natural language processing (NLP) that are used for all other functions. A sampling of language models shown here include OpenAI’s GPT, Google’s LaMDA, and BigScience’s BLOOM.

On the left side of the landscape, we’ve grouped the categories of text summarization, sentiment analysis, and text translation into the overarching category of text analytics, which refers to the process of using AI to analyze unstructured text data for patterns, insights, and intent.

Text summarization companies use AI to summarize written texts into segments of the most important points. Companies in this category include QuillBot, Upword, and spaCy. Sentiment analysis companies use AI to determine the emotions, opinions and tone inherent in written texts. Companies in this category include MonkeyLearn, Repustate, and Cohere. Text translation companies use AI to translate written texts from one language to another. Companies in this category include ModernMT, TextUnited, and Phrase.

Human-like interaction; code, text and search capabilities

In the middle of the landscape, we grouped the categories of virtual assistants, chatbot building platforms, chatbot frameworks, and NLP engines into the overarching category of conversational AI. This includes technologies that communicate with humans using human-like written and verbal communication.

Virtual assistant software responds to human language and assists the user with a variety of tasks and queries. Companies in this category include Augment, Replika and SoundHound. Chatbot building platforms enable non-technical users to create and deploy chatbots without writing code.

Companies in this category include Amelia, Avaamo, and Boost AI. Chatbot frameworks and NLP engines enable developers to create chatbots using code, as well as build core NLP components. Companies in this category include Cognigy, Yellow AI, and Kore AI.

On the right side of the landscape we have the Writers, Coders and Search categories. Writers use AI to create original written content and edit existing written content for grammar and clarity. Companies in this category include Jasper, Writesonic, and Grammarly.

Coders use artificial intelligence to generate code from natural language inputs and debug existing code. Companies in this category include Tabnine, Replit and Mutable AI.

Finally, search includes AI-powered search engines for the entire web or an enterprise’s internal knowledge base. Companies in this category include Neeva, Perplexity AI, and

Ten categories

  • Summary of the text. These companies use artificial intelligence to extract the most important information from long texts and summarize them into short digestible snippets. Other functions of these companies include keyword mining, text classification, and named entity recognition.
  • Analysis of emotions. These companies use artificial intelligence to determine the sentiment of a text as positive, negative, or edgy, as well as the underlying tone, emotion, and intent of the text. Sentiment analysis is often used to analyze customer opinions and brand attitudes.
  • Translation of the text. These companies use artificial intelligence to translate text from one language to another, mostly for written text, but also for audio and video.
  • Virtual assistants. These companies create voice or text assistants that help the user with a variety of tasks, such as taking notes, scheduling appointments, recommending products, and providing mental health therapy.
  • Chatbot building platforms. These companies provide an interface for non-technical users to create and deploy chatbots without the need to write code. They usually include a visual builder to denote the flow of interaction with the chatbot.
  • Chatbot frameworks and NLP engines. These companies provide an environment for developers to build and deploy chatbots using code, as well as companies that build the core natural language processing component that converts human language into machine input.
  • Writers: These companies use AI to generate written text for given topics, such as essays, poems, blog posts, and sales copy. They also help edit and revise written text for grammar, tone, clarity, and style.
  • Encoders. These companies use artificial intelligence to help developers generate code from natural language descriptions. They also help debug existing code and explain the reasoning behind their code edits.
  • Search: These companies use AI to search for answers to general knowledge questions on the web, as well as companies that create custom search solutions for an enterprise’s own internal knowledge base.
  • Language models. These models learn from an abundance of human written and spoken text and predict the probability of the next word in a given sequence of words. They form the fundamental building blocks of NLP used for text-generating and conversational AI.

Broad landscape, evolving challenges

As you can see, the landscape of features like ChatGPT is wide, with a growing number of companies competing in each feature. This infographic shows just a fraction of the 700+ companies we’ve discovered in the space, with more products and companies being launched every day. Like other major technology shifts we’ve seen in the internet, mobile, and more recently crypto, this early spring wave of market accumulation consists of a burst of activity that will continue to accelerate before fizzling out and consolidating in the coming years.

At this stage of market evolution, the obvious challenge for business leaders will be navigating the landscape and identifying the right signals. What are the opportunities that can accelerate their business, deliver new value to their customers, or keep them competitive in a rapidly changing market?

Faced with a plethora of competing generative AI products, business leaders need accurate criteria to weigh and select the right one for their creative and knowledge workforce. It may turn out that a portfolio of solutions will work best, and the role of knowledge and creative workers evolves from creating original content to comparing, collating, and editing the best creative output from multiple AI tools. One thing is certain. every enterprise should have a generative AI plan.

Dong Liu and Nader Ghafari are co-founders Daybreak Insights:.

Special thanks to Arte Merritt for his review and feedback.


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